I've analyzed a (fake) data set ("data") using logistic regression (glm):

logreg1 <- glm(z ~ x1 + x2 + y, data=data, family=binomial("logit"),

na.action=na.pass)

Then, I created a data frame with 2 fixed levels (0 and 1) for each predictor:

attach(data)

x1<-c(0,1)

x2<-c(0,1)

y<-c(0,1)

newdata1<-data.frame(expand.grid(x1,x2,y))

names(newdata1)<-c("x1","x2","y")

Finally, I calculated model-predicted probabilities for each

combination of those fixed levels:

newdata1$predicted <-predict(logreg1,newdata=newdata1, type="response")

I am pretty sure the results I get (see the table below) are actual

probabilities. But just in case - could someone please confirm that

these are probabilities rather than log odds or odds?

Thanks a lot!

x1 x2 y predicted

1 0 0 0 0.08700468

2 1 0 0 0.19262901

3 0 1 0 0.27108334

4 1 1 0 0.48216220

5 0 0 1 0.53686154

6 1 0 1 0.74373367

7 0 1 1 0.81896484

8 1 1 1 0.91887072

--

Dimitri Liakhovitski

Dimitri.Liakhovitski at ninah.com

Dimitri Liakhovitski

Dimitri.Liakhovitski at ninah.com